The Making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)

Abstract

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.

Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps

“…Seminal Seismic PTHA (SPTHA) was performed using crude source and tsunami representations (Lin and Tung, 1982;Rikitake and Aida, 1988;Tinti, 1991). Since then, the methodology has evolved dramatically (Geist and Parsons., 2006;Annaka et al, 2007;Power et al, 2007;Thio et al, 2007;Burbidge et al, 2008;González et al, 2009;Sørensen et al, 2012;Hoechner et al, 2016;Miyashita et al, 2020), also in the framework of large programs (e.g., Horspool et al, 2014;Basili et al, 2021). SPTHA methodology for spatio-temporal and kinematic source treatment and the basic uncertainty framework were mostly transcribed from Probabilistic Seismic Hazard Analysis (PSHA, Esteva, 1967;Cornell, 1968;a historical perspective: McGuire, 2008).…”

“…In this case, spatial characterization provides geometrical and kinematic constraints, such as the fault geometry, preferential slip direction, and other source zone properties. Crustal and general seismicity from unconstrained or unknown faults is treated with a larger uncertainty (e.g., Selva et al, 2016;Basili et al, 2021). Earthquakes are usually simplified to having either uniform (e.g., Horspool et al, 2014) or heterogeneous instantaneous slip (e.g., De Risi and .…”

“…Second, for each individual scenario source, large-scale propagation and coastal inundation modeling (optimally at scales of 1-10 m) need to be represented to quantify tsunami-related on-shore damages and losses. However, the fastest HPC simulation workflows (e.g., de la Asunción et al, 2013;Oishi et al, 2015;Macías et al, 2017;Musa et al, 2018) still require typically 10-60 min to simulate tsunami inundation at a scale of tens of meters, rendering them unsuitable for extensive PTRA studies with up to millions of scenarios (Basili et al, 2021). To overcome this "challenge of scales", modeling approximations are presently necessary for PTHA feasibility and can either involve 1) largely reducing the number of inundation scenarios (e.g., González et al, 2009;Lorito et al, 2015;Volpe et al, 2019;Williamson et al, 2020), 2) use of approximate models or statistics such as amplification factors (e.g., Løvholt et al, 2012;Kriebel et al, 2017;Gailler et al, 2018;Glimsdal et al, 2019), or 3) machine learning-based tsunami emulators (e.g., Sarri et al, 2012;Salmanidou et al, 2017;Giles et al, 2020).…”

“…The third question asked if experts were of the opinion if the research gap existed because of a missing theoretical understanding, a lack of data, or both. While this somewhat ad hoc prioritization is not as solid as a rigorous expert elicitation (e.g., Cooke, 1991;Budnitz et al, 1997;Morgan, 2014; for tsunami hazard see an application in Basili et al, 2021, or the discussion in Grezio et al, 2017) and hence could be somehow biased, we believe it still provides a valuable starting point for future efforts. It is a qualitative broad-brush answer to the question, which research gap may be of highest importance.…”

“…Probabilistic tsunami hazard and risk analyses (PTHA and PTRA, respectively) offer structured and rigorous procedures that allow for tracing and weighting the key elements in understanding the potential tsunami hazard and risk in globally distributed applications (e.g., Basili et al, 2021). Because of this, PTHA are becoming a standard basis for tsunami risk assessment around the world.…”